Contextualizing Internet Memes Across Social Media Platforms

TL;DR

This paper proposes a framework that uses a knowledge graph and vision transformer similarity to identify, map, and analyze internet memes across social media platforms like Reddit and Discord.

Contribution

It introduces a novel approach combining knowledge graphs and vision transformers to holistically track and contextualize internet memes across multiple social media platforms.

Findings

Successfully created a data lake of meme posts from Reddit and Discord.

Demonstrated effective matching of memes to the IMKG knowledge graph.

Showed potential for analyzing meme prevalence and context across platforms.

Abstract

Internet memes have emerged as a novel format for communication and expressing ideas on the web. Their fluidity and creative nature are reflected in their widespread use, often across platforms and occasionally for unethical or harmful purposes. While computational work has already analyzed their high-level virality over time and developed specialized classifiers for hate speech detection, there have been no efforts to date that aim to holistically track, identify, and map internet memes posted on social media. To bridge this gap, we investigate whether internet memes across social media platforms can be contextualized by using a semantic repository of knowledge, namely, a knowledge graph. We collect thousands of potential internet meme posts from two social media platforms, namely Reddit and Discord, and develop an extract-transform-load procedure to create a data lake with candidate meme posts. By using vision transformer-based similarity, we match these candidates against the memes cataloged in IMKG -- a recently released knowledge graph of internet memes. We leverage this grounding to highlight the potential of our proposed framework to study the prevalence of memes on different platforms, map them to IMKG, and provide context about memes on social media.